{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array([1.,2.,3.,4.,5.])\n",
    "y=np.array([1.,3.,2.,3.,5.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.axis([0,6,0,6])#坐标轴范围\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n",
      "0.39999999999999947\n"
     ]
    }
   ],
   "source": [
    "x_mean=np.mean(x)\n",
    "y_mean=np.mean(y)\n",
    "num=0.0#求a分子\n",
    "d=0.0#求a分母\n",
    "for x_i,y_i in zip(x,y):\n",
    "    num=num+(x_i-x_mean)*(y_i-y_mean)#a分子累加求和\n",
    "    d=d+(x_i-x_mean)**2#a分母累加求和\n",
    "a=num/d#求出参数a\n",
    "b=y_mean-a*x_mean#求出参数b\n",
    "print(a)\n",
    "print(b)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制图像\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat=a*x+b\n",
    "plt.scatter(x,y,color=\"blue\")\n",
    "plt.plot(x,y_hat,color=\"red\")\n",
    "plt.axis([0,6,0,6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.2\n"
     ]
    }
   ],
   "source": [
    "#预测新点\n",
    "x_predict=6\n",
    "y_predict=a*x_predict+b\n",
    "print(y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用自己的SimpleLinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.8, 4.4, 5.2, 6. , 2.8])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from SimpleLinearRegression import SimpleLinearRegression1\n",
    "reg1=SimpleLinearRegression1()#创建对象\n",
    "reg1.fit(x,y)#训练\n",
    "reg1.predict(np.array([3,5,6,7,3]))#预测[需要传入数组ndarray对象]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n",
      "0.39999999999999947\n"
     ]
    }
   ],
   "source": [
    "#显示训练的系数\n",
    "print(reg1.a_)\n",
    "print(reg1.b_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f1d9261550>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.plot(x,reg1.predict(x),color=\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "向量化实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n",
      "0.39999999999999947\n",
      "[ 8.4  9.2 10. ]\n"
     ]
    }
   ],
   "source": [
    "from SimpleLinearRegression import SimpleLinearRegression2\n",
    "reg2=SimpleLinearRegression2()\n",
    "reg2.fit(x,y)\n",
    "print(reg2.a_)\n",
    "print(reg2.b_)\n",
    "y_hat2=reg2.predict(np.array([10,11,12]))\n",
    "print(y_hat2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "向量化计算性能测试\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "m=1000000\n",
    "big_x=np.random.random(size=m)#大小为100万的数组\n",
    "big_y=big_x*2.0+3.0+np.random.normal(size=m)#加上服从正态分布的随机数（噪声）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "565 ms ± 19.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "14.8 ms ± 292 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit reg1.fit(big_x,big_y)\n",
    "%timeit reg2.fit(big_x,big_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.00301480977887"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1.a_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.003014809778755"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2.a_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.998147028327894"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2.b_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.998147028327894"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2.b_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
